docs: def execute(self, style, xpoints, ypoints, zpoints, mask=None,
                  backend='vectorized', n_closest_points=None):
          """Calculates a kriged grid and the associated variance.
  
          This is now the method that performs the main kriging calculation.
          Note that currently measurements (i.e., z values) are
          considered 'exact'. This means that, when a specified coordinate
          for interpolation is exactly the same as one of the data points,
          the variogram evaluated at the point is forced to be zero.
          Also, the diagonal of the kriging matrix is also always forced
          to be zero. In forcing the variogram evaluated at data points
          to be zero, we are effectively saying that there is no variance
          at that point (no uncertainty, so the value is 'exact').
  
          In the future, the code may include an extra 'exact_values' boolean
          flag that can be adjusted to specify whether to treat the
          measurements as 'exact'. Setting the flag to false would indicate
          that the variogram should not be forced to be zero at zero distance
          (i.e., when evaluated at data points). Instead, the uncertainty in the
          point will be equal to the nugget. This would mean that the diagonal
          of the kriging matrix would be set to the nugget instead of to zero.
  
          Parameters
          ----------
          style : str
              Specifies how to treat input kriging points.
              Specifying 'grid' treats xpoints, ypoints, and zpoints as arrays of
              x, y, and z coordinates that define a rectangular grid.
              Specifying 'points' treats xpoints, ypoints, and zpoints as arrays
              that provide coordinates at which to solve the kriging system.
              Specifying 'masked' treats xpoints, ypoints, and zpoints as arrays
              of x, y, and z coordinates that define a rectangular grid and uses
              mask to only evaluate specific points in the grid.
          xpoints : array_like, shape (N,) or (N, 1)
              If style is specific as 'grid' or 'masked', x-coordinates of
              LxMxN grid. If style is specified as 'points', x-coordinates of
              specific points at which to solve kriging system.
          ypoints : array-like, shape (M,) or (M, 1)
              If style is specified as 'grid' or 'masked', y-coordinates of
              LxMxN grid. If style is specified as 'points', y-coordinates of
              specific points at which to solve kriging system.
              Note that in this case, xpoints, ypoints, and zpoints must have the
              same dimensions (i.e., L = M = N).
          zpoints : array-like, shape (L,) or (L, 1)
              If style is specified as 'grid' or 'masked', z-coordinates of
              LxMxN grid. If style is specified as 'points', z-coordinates of
              specific points at which to solve kriging system.
              Note that in this case, xpoints, ypoints, and zpoints must have the
              same dimensions (i.e., L = M = N).
          mask : boolean array, shape (L, M, N), optional
              Specifies the points in the rectangular grid defined by xpoints,
              ypoints, zpoints that are to be excluded in the
              kriging calculations. Must be provided if style is specified
              as 'masked'. False indicates that the point should not be masked,
              so the kriging system will be solved at the point.
              True indicates that the point should be masked, so the kriging
              system should will not be solved at the point.
          backend : str, optional
              Specifies which approach to use in kriging. Specifying 'vectorized'
              will solve the entire kriging problem at once in a
              vectorized operation. This approach is faster but also can consume a
              significant amount of memory for large grids and/or large datasets.
              Specifying 'loop' will loop through each point at which the kriging
              system is to be solved. This approach is slower but also less
              memory-intensive. Default is 'vectorized'.
          n_closest_points : int, optional
              For kriging with a moving window, specifies the number of nearby
              points to use in the calculation. This can speed up the calculation
              for large datasets, but should be used with caution.
              As Kitanidis notes, kriging with a moving window can produce
              unexpected oddities if the variogram model is not carefully chosen.
  
          Returns
          -------
          kvalues : ndarray, shape (L, M, N) or (N, 1)
              Interpolated values of specified grid or at the specified set
              of points. If style was specified as 'masked', kvalues will be a
              numpy masked array.
          sigmasq : ndarray, shape (L, M, N) or (N, 1)
              Variance at specified grid points or at the specified set of points.
              If style was specified as 'masked', sigmasq will be a numpy
              masked array.
          """
  
          if self.verbose:
              print("Executing Ordinary Kriging...\n")
  
          if style != 'grid' and style != 'masked' and style != 'points':
              raise ValueError("style argument must be 'grid', 'points', "
                               "or 'masked'")
  
          xpts = np.atleast_1d(np.squeeze(np.array(xpoints, copy=True)))
          ypts = np.atleast_1d(np.squeeze(np.array(ypoints, copy=True)))
          zpts = np.atleast_1d(np.squeeze(np.array(zpoints, copy=True)))
          n = self.X_ADJUSTED.shape[0]
          nx = xpts.size
          ny = ypts.size
          nz = zpts.size
          a = self._get_kriging_matrix(n)
  
          if style in ['grid', 'masked']:
              if style == 'masked':
                  if mask is None:
                      raise IOError("Must specify boolean masking array when "
                                    "style is 'masked'.")
                  if mask.ndim != 3:
                      raise ValueError("Mask is not three-dimensional.")
                  if mask.shape[0] != nz or mask.shape[1] != ny or mask.shape[2] != nx:
                      if mask.shape[0] == nx and mask.shape[2] == nz and mask.shape[1] == ny:
                          mask = mask.swapaxes(0, 2)
                      else:
                          raise ValueError("Mask dimensions do not match "
                                           "specified grid dimensions.")
                  mask = mask.flatten()
              npt = nz * ny * nx
              grid_z, grid_y, grid_x = np.meshgrid(zpts, ypts, xpts, indexing='ij')
              xpts = grid_x.flatten()
              ypts = grid_y.flatten()
              zpts = grid_z.flatten()
          elif style == 'points':
              if xpts.size != ypts.size and ypts.size != zpts.size:
                  raise ValueError("xpoints, ypoints, and zpoints must have "
                                   "same dimensions when treated as listing "
                                   "discrete points.")
              npt = nx
          else:
              raise ValueError("style argument must be 'grid', "
                               "'points', or 'masked'")
  
          xpts, ypts, zpts = \
              _adjust_for_anisotropy(np.vstack((xpts, ypts, zpts)).T,
                                     [self.XCENTER, self.YCENTER, self.ZCENTER],
                                     [self.anisotropy_scaling_y, self.anisotropy_scaling_z],
                                     [self.anisotropy_angle_x, self.anisotropy_angle_y, self.anisotropy_angle_z]).T
  
          if style != 'masked':
              mask = np.zeros(npt, dtype='bool')
  
          xyz_points = np.concatenate((zpts[:, np.newaxis], ypts[:, np.newaxis],
                                       xpts[:, np.newaxis]), axis=1)
          xyz_data = np.concatenate((self.Z_ADJUSTED[:, np.newaxis],
                                     self.Y_ADJUSTED[:, np.newaxis],
                                     self.X_ADJUSTED[:, np.newaxis]), axis=1)
          bd = cdist(xyz_points, xyz_data, 'euclidean')
  
          if n_closest_points is not None:
              from scipy.spatial import cKDTree
              tree = cKDTree(xyz_data)
              bd, bd_idx = tree.query(xyz_points, k=n_closest_points, eps=0.0)
              if backend == 'loop':
                  kvalues, sigmasq = \
                      self._exec_loop_moving_window(a, bd, mask, bd_idx)
              else:
                  raise ValueError("Specified backend '{}' not supported "
                                   "for moving window.".format(backend))
          else:
              if backend == 'vectorized':
                  kvalues, sigmasq = self._exec_vector(a, bd, mask)
              elif backend == 'loop':
                  kvalues, sigmasq = self._exec_loop(a, bd, mask)
              else:
                  raise ValueError('Specified backend {} is not supported for '
                                   '3D ordinary kriging.'.format(backend))
  
          if style == 'masked':
              kvalues = np.ma.array(kvalues, mask=mask)
              sigmasq = np.ma.array(sigmasq, mask=mask)
  
          if style in ['masked', 'grid']:
              kvalues = kvalues.reshape((nz, ny, nx))
              sigmasq = sigmasq.reshape((nz, ny, nx))
  
          return kvalues, sigmasq